{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equilibrium Properties and Partial Ordering (Al-Fe and Al-Ni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only needed in a Jupyter Notebook\n",
    "%matplotlib inline\n",
    "# Optional plot styling\n",
    "import matplotlib\n",
    "matplotlib.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pycalphad import equilibrium\n",
    "from pycalphad import Database, Model\n",
    "import pycalphad.variables as v\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Al-Fe (Heat Capacity and Degree of Ordering)\n",
    "Here we compute equilibrium thermodynamic properties in the Al-Fe system. We know that only B2 and liquid are stable in the temperature range of interest, but we just as easily could have included all the phases in the calculation using `my_phases = list(db.phases.keys())`. Notice that the syntax for specifying a range is `(min, max, step)`. We can also directly specify a list of temperatures using the list syntax, e.g., `[300, 400, 500, 1400]`.\n",
    "\n",
    "We explicitly indicate that we want to compute equilibrium values of the `heat_capacity` and `degree_of_ordering` properties. These are both defined in the default `Model` class. For a complete list, see the documentation. `equilibrium` will always return the Gibbs energy, chemical potentials, phase fractions and site fractions, regardless of the value of `output`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 34, X_AL: 1, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 350.0 400.0 450.0 500.0 550.0 ...\n",
      "  * X_AL                (X_AL) float64 0.25\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'FE'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 1.0 nan 1.0 nan 1.0 nan ...\n",
      "    GM                  (P, T, X_AL) float64 -2.858e+04 -2.994e+04 -3.15e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -7.274e+04 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 0.25 0.75 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 0.5 0.5 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U6 'B2_BCC' '' 'B2_BCC' '' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 0.6666 nan 0.6665 nan ...\n",
      "    heat_capacity       (P, T, X_AL) float64 25.45 26.93 28.47 30.18 32.16 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.7+5.g20149e02.dirty\n",
      "    created:  2018-04-18T19:27:07.389851\n"
     ]
    }
   ],
   "source": [
    "db = Database('alfe_sei.TDB')\n",
    "my_phases = ['LIQUID', 'B2_BCC']\n",
    "eq = equilibrium(db, ['AL', 'FE', 'VA'], my_phases, {v.X('AL'): 0.25, v.T: (300, 2000, 50), v.P: 101325},\n",
    "                 output=['heat_capacity', 'degree_of_ordering'])\n",
    "print(eq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also compute degree of ordering at fixed temperature as a function of composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 1, X_AL: 100, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 700.0\n",
      "  * X_AL                (X_AL) float64 1e-09 0.01 0.02 0.03 0.04 0.05 0.06 ...\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'FE'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 1.0 nan 1.0 nan 1.0 nan ...\n",
      "    GM                  (P, T, X_AL) float64 -2.447e+04 -2.564e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -2.312e+05 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 1e-09 1.0 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 9.999e-10 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U6 'B2_BCC' '' 'B2_BCC' '' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 4.028e-05 nan 7.14e-17 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.7+5.g20149e02.dirty\n",
      "    created:  2018-04-18T19:27:24.936901\n"
     ]
    }
   ],
   "source": [
    "eq2 = equilibrium(db, ['AL', 'FE', 'VA'], 'B2_BCC', {v.X('AL'): (0,1,0.01), v.T: 700, v.P: 101325},\n",
    "                  output='degree_of_ordering')\n",
    "print(eq2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "Next we plot the degree of ordering versus temperature. We can see that the decrease in the degree of ordering is relatively steady and continuous. This is indicative of a second-order transition from partially ordered B2 to disordered bcc (A2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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beob48zNmSMWqshIOatwTNFbuU03DvDJrnvnAedrFedrFpudUdxKjTDKGRBqh\nHp0ujO2mM1FZai78BzXufQcyNJJkS/cQT3Xu5pmOQZ7u3E37QIJHdw7w6M497R6aqstYuU81hzRW\nItE4+0YrrD0jkgvc924X52mXfD0nsTxl+k3AecB/Aq3AUuDTwG+tmRQpYWw3PROqyiIc1jKfw1r2\ndBHQu3uEpzt383THIE93DvJM5246B0fo3LqLv22FHz/YRmN1GccsruGoRTUctbiGuqqyvLtPhfve\n7eI87ZKX5yRUtXV8WkQ+ARyrqru8Rc+KyEPAQ8APM30+HRFZDXwHc+fxU1X9eoY0/wxcirmDeVRV\n3+bzOAKjGMonobA86+aVceLSKCcujQKmrmPbrmGe7Bjkvq1dPNmVoGtwhFuf7Zl4CHL/+iqOXlzD\n0YtrOKJlPhWlwY68W0j5ORXO0y5h9PTbuikKzAN2pSyb5y2fFm/gou9jntreBjwoIjep6pMpaVYA\nlwCvUdVeEdnHp1ugRCLFUdpWyJ4lIuxXV8l+dZUcv08J0QUL2NozxIbt/WzY3s/jbQMT9Ro3PN5B\nWUQ4vHk+r12+gNcsiwZyl1HI+ZmK87RLGD39BolrgL+IyLcxXYXvC3zUW+6H44DNqroFQESuA84B\nnkxJ8z7g+6raC6CqHT63HSh9fX3U1dUFrTEtxeZ5QMM8DmiYx/mvaDbPebQPsmF7Hw9v7+f57iE2\n7uhn445+vnfvSxzRMp/XLV/AScsWUDcvPwGj2PKz0HGedrHp6TdIfArYDPwLsAjYCXwP/92HL8YE\nl3G2AcenpTkIQET+gSmSulRVb0nfUEdHB2vXrqW0tJRkMsmaNWtYt24dbW1tVFdXE4lE6Ovro6mp\niZ6eHlSVpqYm2tvbJ7rOHRgYoLm5mc7OTkSE+vp6Ojs7qa2tJZlMMjg4SEtLC21tbZSVlRGNRunq\n6iIajZJIJBgaGppYP7697u5u6urqGBoaIh6PT6yvrKykqqqK3t5eGhoa6O/vJ5FITKyvqqqivLyc\nWCxGY2MjsViMkZGRifW2jmlkZITh4WFfx1ReXk5NTU0gxzQyMkJ3d/fLjqlJhAsOr+fU5lGoaOTh\nHYP848V+nuoZmagE//76baxYUMpJy6Ic1VRGhSZydkyqSmtrq/XvaSbnnt9jam1tDfTc83NM0WiU\n1tbWQM89P8dUUVFBV1dXTr4nm8eUTCaJx+N7HdNsEdWpGzB5RUVfJIsxrkXkfOB0Vb3Im78QOE5V\nP5KS5k+YIVL/GVgC/A04PKUeBID169frypUrZ6ORE7Zt28aSJUuC1piWueo5MDzKva0x/rZ1Fw9v\n72d0zJzPAuYOY/8FnLx/HdGBd13KAAAgAElEQVRKv/+HcuMZFM7TLsXquWHDhodXrVp17Gy2Ne0v\nR1WTIrIOU6E8W7ZhiqjGWQLsyJDmPlUdAbaKyDPACuDBLPabc6YLsoXCXPWcX1HKaQc1cNpBDQwM\nj7L+xRj3bNnFhu39PNY2wGNtA/zovu2ctHwBZx3SyOHN1Vaa1s7V/AwK52kXm54zqZO4GPjBLPfz\nILBCRJYD24G3Auktl36PGa/iahFpxBQ/bZnl/vJGU1NT0Aq+CIPn/IpS3riigTeuaGAwkWR9a4y7\ntvTy4Et9/PX5Xv76fC9LF1TypkMaOfXAOuZXzP7uIgz5mU+cp11sevptR3gc8B0ReUFE/iYi94y/\n/HxYVUeBDwO3Ak8B16vqJhH5soic7SW7FegWkSeBvwKfVNXumR1O/mlvbw9awRdh86wuj3Dqinq+\nevoB/PxfDuNtRzZTX1VK6644P1i/jQuufYJv3dPKM52DgXrmGudplzB65m2Ma1W9GdObbOqyL6RM\nK6YjwU9ks598Uwzj3UK4PZtrynn3sYt4x9ELubd1F//3VBcbdwxMPIexorGKs1Y2csoBdb67Qg9z\nfuYC52kXm55ujGtHaCgtEV63vI7XLa9jWyzOzU93c+uz3TzXNcQVf3+JHz+wg3MPa+Lcw5uoyaIo\nyuGYS/h+bFVE3iMid4rIM977e3IpViwMDBTFuEvOM40l0Uref/xifnXB4Xzq5KUcuk81g4kkv9jY\nxoXXbeLqh3bQFx8N3DNbnKddwujp6++SiHwWM3bEt9jTd9OnRGSRql5mzaYIaW5uDlrBF84zM+Wl\nJZy6op5TV9TzeNsAv9iwk407Brj2kXZ+v6mTcw5t4i1H7ENtWhNal592cZ52senp907iIuA0Vf2x\nqt6qqj8GVgPvt2ZSpHR2dgat4AvnOT1HtMzn8jNXcMVZKzh6cQ27R8b41aPtXPjrTfzswR3EUu4s\nXH7axXnaxaan34LXarxxJFLoBopjVPAcUsjdWafiPP1zWMt8vn7GgTzZPsgvNu7koW39/PrRdv6w\nqZOzD23kvCP2KQhPPzhPu4TR0++dxC3AL0XkYBGpEpGVmGcnbrVmUqTU19cHreAL5zlzDm2u5mur\nD+Q7Zx/Eq5bUEh8d4/rHOrjw109yy7YxhkcLf7ytQsrPqXCedrHp6TdIfBjoBx7FjG/9CDAIfGSq\nD4WBMN5+5pJC9Dxkn2ouW30A3z37II7ft5bh0TGuf6Kbi254ivWtsaD1pqQQ8zMTztMuNj19BQlV\n7VPVd2K6B18IzFPVd6b3qxRGamtrg1bwhfPMnpX7VPOV0w/girNWsF+0jPaBBF+8fQtfvG0Lbf2z\n6tYs5xRyfqbiPO1i03NGI7eo6piqdoR9XOtUkslk0Aq+cJ72OKxlPped3MzFJyxmXlkJ61+M8b4b\nnuLajW0kkoX10yiG/ATnaRubnsEO7zUHGBycXbcO+cZ52iU+tJs1h+/Dz847lNcfUMdwUrn64Z1c\nfOPTPLytL2i9CYolP52nXWx6uiCRJWEcGD2XFJtnQ3UZl7x+GZefeSD7RivYFhvmklue57I7ttI1\nmAhWkuLLz0InjJ6TBgkR+WbK9Bus7XGOMT7wUKHjPO2S7nnUohquXLOS975qIRWlJdy9dRdrb3iK\nGx7vIDkWXPfSxZqfhUoYPae6k0h9UO731vY4xygry//4yrPBedolk2dZpIS3vrKFn77lEF69NMrQ\nyBg/vn87/3HL8/TuHgnAsrjzsxAJo+dUD9M9KiI3YMahrhCRL2dKlNqTaxiJRqNBK/jCedplKs/m\nmnIufeP+3PdijG/d8yIbd/Tzwd89zadfv4yjFtXkT5K5kZ+FRBg9p7qTOA/zPMRCzGiQ+2Z4Ff44\nfjmmq6sraAVfOE+7+PE8Yb8oV567kle0zKdnaJTP3LyZnz+8M6/FT3MpPwuBMHpOeiehqh3AVwFE\npFRVXa+vGQjjP4tcMtc8G6rLuPzMA/nFxjau3djGLza28XjbAJe8fhn183JfdDHX8jNowujp92G6\n94hInYi8U0Qu8d6L4/n0HJNIBN+CxQ/O0y4z8YyUCO86ZiH/ecYB1FWV8ujOAS6+8Wk2bM99U9m5\nmJ9BEkZPX0FCRE4EnseMc/0K4APAZm95qBkaGgpawRfO0y6z8Tx6cS0/PHclRy6az674KJf8+Xmu\nyXHx01zOzyAIo6ff5yS+DXxIVV+tqheo6muADwLftWZSpISx3XQumeue9fPK+M/VB3Lh0ebzv9zY\nxqdv3kz3YG5aP831/Mw3YfT0GyQOAq5PW3YDcKA1kyIljO2mc0kYPCMlwoVHL+TyMw+kvqqUx9oG\nuPh3T7NxR79FQ0MY8jOfhNHTb5B4Dnhr2rLzMUVQoaa8vDxoBV84T7vY8DxyUQ0/PHclRy2qIRYf\n5XO3PM/fX7DbZ2aY8jMfhNHTb5D4GPA9EblPRH4tIvcDPwA+as2kSKmpyW+799niPO1iy7NuXhlf\nW30Abz6siZEx5at3bOX257qtbBvCl5+5Joyefls33QscAHwPeBj4b+BAb3mo6e6294POJc7TLjY9\nIyXCB09YzDuOamFM4Zt3v8gfNtkZDyCM+ZlLwujpd/hSVLUX+IW1Pc8R6urqglbwhfO0i21PEeGd\nxyxkXnmEH9+/ne+v38ZgIskFRzZnNRRlWPMzV4TR0/UCmyVhbBKXS8Lued4R+/Dxk/ZFgKsf3slP\nH9iB6uybyIY9P20TRk8XJLIkHo8HreAL52mXXHqesbKRS16/jIjAbx7v4Dv/eGnWz1K4/LRLGD1d\nkMiSMLabziXO03DKAXV86bT9KY8INz/dzeV3vcDoLAKFy0+7hNHTd5AQkTIRea2I/Is3Xy0i1TP4\n/GoReUZENovIZ6ZId56IqIgc63fbQRLGdtO5xHnu4bh9o3xt9QHMKyvhri27+NLtWxgendnwqC4/\n7RJGT7/dchwBPAv8BPiZt/hk4Cqfn48A3wfOAA4FLhCRQzOkq8E0q73fz3YLgcrKyqAVfOE87ZIv\nz1csrOEbZ66gtiLC/S/18blbn2d3wv/4xS4/7RJGT793Ej8EvqCqK4Hx/gPuBk7y+fnjgM2qukVV\nE8B1wDkZ0n0F+AZQHAV/QFVVVdAKvnCedsmn50FN8/jWWSuon2c6B/z0nzf7DhQuP+0SRk+/QeIw\n9jR/VQBVHQT8miwGXkqZ3+Ytm0BEjgL2VdU/+dxmQdDb2xu0gi+cp13y7bm0roorzjqIlppynunc\nzeV3tzLmo9WTy0+7hNHT73MSLwDHAA+NLxCR44DNPj+fqaH3xBkuIiXAFcC7p9tQR0cHa9eupbS0\nlGQyyZo1a1i3bh1tbW1UV1cTiUTo6+ujqamJnp4eVJWmpiba29uZP38+AAMDAzQ3N9PZ2YmIUF9f\nT2dnJ7W1tSSTSQYHB2lpaaGtrY2ysjKi0ShdXV1Eo1ESiQRDQ0MT61WVgYEBuru7qaurY2hoiHg8\nPrG+srKSqqoqent7aWhooL+/n0QiMbG+qqqK8vJyYrEYjY2NxGIxRkZGJtbbOqZEIsHw8LCvYyov\nL6empiaQY0okEhMPAtn8nmwf09jYGK2trda/p6mOKZJI8JFXVPG1B0dZ3xrjynue453HLJrymCor\nK2ltbQ303PPzPdXW1tLa2hrouefnmMrKyujq6gr03PNzTKOjo8Tj8b2OabaInzbYInIWpi7iSuDf\ngMsw3Ya/T1Vv8/H5E4FLVfV0b/4SAFX9T28+iukHasD7SAvQA5ytqg+lbmv9+vW6cuVKXweXD3bu\n3MnChQuD1pgW52mXID0f2mbqJsYUvrBqOSctXzBpWpefdilWzw0bNjy8atWqWTUG8tstx58wlc5N\nmLqIpcAaPwHC40FghYgsF5FyTGeBN6VsP6aqjaq6TFWXAfeRIUAUImEchCSXOM/pOXZJLWtftQiA\nb9zdytaeyR+ccvlplzB6+m4Cq6obVPVDqvomVb1YVR+ewWdHgQ8DtwJPAder6iYR+bKInD1z7cIh\njO2mc4nz9Md5R+zDGw6oIz46xqW3b6EvPpoxXdCefnGedsn7cxIiUiEil4nIFhGJectOE5EP+92R\nqt6sqgep6gGqepm37AuqelOGtKcUw10EhLPddC5xnv4QET7+2v04sKGKnf0JLrvzhYxPZQft6Rfn\naZcgxpO4AjgceDt7Kpw3YUanCzVhbBKXS5ynfypKS7j0jfuzoLKUjTv6+ekD21+WphA8/eA87RJE\nE9hzgbep6npgDEBVt5PWjDWMhHEQklziPGfGPvPL+fypy4kI/PaJTv7yXM9e6wvFczqcp12CGHQo\nQVpzWRFpAoqjc/UcEovFglbwhfO0SyF5HtEyn3Wv3heAK/7+Is90Dk6sKyTPqXCedrHp6TdI/Aa4\nRkSWA4jIQswARNdZMylSGhsbg1bwhfO0S6F5nnVII29a2cBIUvnS7Vvp2W06Rig0z8lwnnax6ek3\nSPwH5oG6x4EFmDGvdwBfsmZSpITxn0UucZ6z50MnLuHw5mq6do/w5b9sJZEcK0jPTDhPu+T1TsJ7\nGvok4NOqOh9oBmpU9eNeP0yhZmRkZPpEBYDztEshepZFSvj8quU0VpfxZMcg3793W9G06y/E/MxE\nGD2nDRKqOgb8QVWHvflOzWaorDlGGNtN5xLnmR1188q49FQzDsWfn+nmsYHi6LW0UPMznTB6+i1u\nukdETrC21zlEGNtN5xLnmT0HNc3jYyftB8BVD7VN+qBdIVHI+ZlKGD39dvDXCvxZRP6A6c114k5C\nVb9gzaYIqa72Pe5SoDhPuxS656oD67j9uR427ujn2kfauPiEJUErTUmh5+c4YfT0eydRBfweExyW\nAPumvEJNJBIJWsEXztMuhe4pIrz/+EUIcNOTXezoGw5aaUoKPT/HCaOn3w7+3jPZy5pJkdLX1xe0\ngi+cp12KwfOAhnmcsLCc0THlfx7cEbTOlBRDfkI4Pf323bT/JK/FXuun0NLU1BS0gi+cp12KxXPt\n8Usojwh3b93FUx2D038gIIolP8Po6fcCvxnzbMRzadMvAsMi8lsRabZmVUT09PRMn6gAcJ52KRbP\nkng/bzl8HwB+fP92CrVhYrHkZxg9/QaJ9wG/BA4CKoGDMcOZfgg4AlMB/n1rVkVEof7o0nGedikm\nz39+ZTPRylI2tQ/yjxcK82GwYsrPYsCmp98g8SXg/ar6vKomVHUzpgfYz6vq05hhR0+xZlVEhPH2\nM5c4T7s0NTVRXR7hwqNNu/mfPriDkeRYwFYvp5jysxgIoripBFiWtmw/YLwKfQD/zWnnFO3t7UEr\n+MJ52qXYPM9c2ciSaAU7+ob5v6cLr1/OYsvPQsemp98g8W3gTm/goYtF5KvAHd5ygDcB661ZFRHZ\nDjKeL5ynXYrNs7REuOg4M+TpLzbsZDCRDFLrZRRbfhY6Nj39NoH9BvBeoAU4B1gErFXVy731v1fV\nM6xZORwO65y4X5QjWubTN5zkukeK48lhR/DMZIzrW1R1raqeoarvVdVbcilWLAwMDASt4AvnaZdi\n9Bx/wA7gxk2ddAwUTud/xZifhYxNz7yNcT1XaW4ujpa/ztMuxep5cFM1rz+gjpGk8j8PFc4DdsWa\nn4WKTU83xnWWdHZ2Bq3gC+dpl2L2fM+xCykrEe7Y3MuzXbsDsHo5xZyfhYhNTzfGdZaISNAKvnCe\ndilmz5aaCs45zDSR/EmBPGBXzPlZiNj0dGNcZ0l9fX3QCr5wnnYpds8LjmympiLCozsHuP+l4Psj\nKvb8LDRseroxrrMkjLefucR52mUyz5qKUt5+lPeA3QM7SI4FezdR7PlZaARR3OTGuJ6E2traoBV8\n4TztMhc8/+mQRhbWlPPirjh/fibYQoG5kJ+FhE1Pv89JJFT1Y26M65eTTBbWQ0mT4TztMhc8yyIl\nrH2VaRL7vxt2MjQS3DHNhfwsJGx6+m0Ce6iIfEBELgHWAIdYMyhyBgcLt/vlVJynXeaK52uXL+DA\nhip6h0bZuKM/T1YvZ67kZ6Fg03PKICGGqzDFTP8BnA18FnhMRP5HZlCFLiKrReQZEdksIp/JsP4T\nIvKkiDwmIneIyNIZHksghHFg9FziPO0ynaeIcMJ+UQAe2RHcg2JzJT8LBZue091JvB/Tu+sJqrpU\nVU9U1f2AE4HXAh/wsxMRiWC6Ej8DOBS4QEQOTUu2EThWVV8B3AB8w/dRBEgYB0bPJc7TLn48j1xk\n+vl5NMA7ibmUn4WATc/pgsSFwEdV9cHUhd78x7z1fjgO2KyqW7x6jOswfUClbvOvqjr+ZM99mLG0\nC56ysrKgFXzhPO0ylzxX7lNNeUTY2hund2gkD1YvZy7lZyFg03O6IHEocPck6+721vthMfBSyvw2\npn4Qby3wZ5/bDpRoNBq0gi+cp13mkmd5pITDmqsBeGxnMEVOcyk/CwGbntONARFR1Yz3oKraP4Px\nrTPVXWRsmC0i7wCOBU7OtL6jo4O1a9dSWlpKMplkzZo1rFu3jra2Nqqrq4lEIvT19dHU1ERPTw+q\nSlNTE+3t7RPd5w4MDNDc3ExnZyciQn19PZ2dndTW1pJMJhkcHKSlpYW2tjbKysqIRqN0dXURjUZJ\nJBIMDQ1NrB8YGGDp0qV0d3dTV1fH0NAQ8Xh8Yn1lZSVVVVX09vbS0NBAf38/iURiYn1VVRXl5eXE\nYjEaGxuJxWKMjIxMrLd1TDt27ODggw/2dUzl5eXU1NQEckzPP/88ixcvtv492T6mrVu3UlNTY/17\nsn1MsViMioqKaY/p0MYKNu4Y4O/P7uSExfPyfkzJZJJIJBLouefnmIaGhmhsbAz03PNzTL29vaxY\nsWKvY5otMtUj+SKyGzNWxGQV1H9U1eppdyJyInCpqp7uzV8CoKr/mZbuVOC/gZNVtSPTttavX68r\nV66cbpd5Y9euXSxYsCBojWlxnnaZa55PdQzyrzc9y5JoBVed77eAwB5zLT+DJt1zw4YND69aterY\n2WxrujuJDuCqadb74UFghffE9nbgrcDbUhOIyFHAj4DVkwWIQiSRKI5HRZynXeaa54rGeVSVlbAt\nNkzXYILG6vIcm+3NXMvPoLHpOWWQUNVlNnaiqqNet+K3YoY8vUpVN4nIl4GHVPUm4JvAfOA3Xsva\nF1X1bBv7zyVDQ0NBK/jCedplrnmWlghHtMzngZf6eGTHAKeuyG8fRXMtP4PGpmfexqVW1ZuBm9OW\nfSFl+tR8udgkjO2mc4nztMtMPF+50ASJR3f25z1IzMX8DJJ8PifhmIYwtpvOJc7TLjPxPHJRDRDM\nQ3VzMT+DJJ/PSTimobw8v2W3s8V52mUueu5fX0VNRYT2gQQ7+4dzaPVy5mJ+BolNTxcksqSmpiZo\nBV84T7vMRc+IVy8B8Gie7ybmYn4GiU1PFySypLu7OMZdcp52mauee4qc8ttFx1zNz6Cw6emCRJbU\n1dUFreAL52mXuer5yoXmTuKRnf15HdZ0ruZnUNj0dEEiS8LYJC6XOE+7zNRzWV0l0cpSenaPsi2W\nv3qJuZqfQWHT0wWJLInH40Er+MJ52mWueooIR47fTeSxyGmu5mdQ2PR0QSJLwthuOpc4T7vMxvOV\nXr3Eo3ns7G8u52cQuOckCogwtpvOJc7TLrPxPGp8fImdA4zlqV5iLudnELjnJAqIysrKoBV84Tzt\nMpc9F9VW0DivjFh8lNbe/BSvzOX8DAKbni5IZElVVVXQCr5wnnaZy54iMjFaXb7qJeZyfgaBTU8X\nJLKkt7c3aAVfOE+7zHXPV+a5i465np/5xqanCxJZ0tDQELSCL5ynXea655ELTZB4rG2A5Fju6yXm\nen7mG5ueLkhkSX9/cIPHzwTnaZe57tlcU05LTTmDiSTPd+f+2YC5np/5xqanCxJZEsZBSHKJ87RL\nNp7jdxOP7Mz9hTEM+ZlPbHq6IJElYWw3nUucp12y8cxn5XUY8jOfuOckCogwtpvOJc7TLtl4jlde\nP9E2yGiO6yXCkJ/5xD0nUUCEsUlcLnGedsnGs2FeGftGK4iPjvFM56BFq5cThvzMJ64JbAERxkFI\nconztEu2nvlqChuW/MwXbtChAiIWiwWt4AvnaZeweB450UVHbuslwpKf+cKmpwsSWdLY2Bi0gi+c\np13C4vlKr4XTpvZBEqNjNpQyEpb8zBc2PV2QyJIw/rPIJc7TLtl6RitL2b++kpGk8lRH7uolwpKf\n+cLdSRQQIyMjQSv4wnnaJUyeE/USOew6PEz5mQ9serogkSVhbDedS5ynXWx4TjxUl8PnJcKUn/nA\nPSdRQISx3XQucZ52seF5REs1JQJPdwwyNJK0YPVywpSf+cA9J1FAVFdXB63gC+dplzB5zq8o5cCG\neSTVVGDngjDlZz6w6emCRJZEIpGgFXzhPO0SNs+JprA5KnIKW37mGpueLkhkSV9fX9AKvnCedgmb\n55E5rrwOW37mGpueeQsSIrJaRJ4Rkc0i8pkM6ytE5Nfe+vtFZFm+3LKhqakpaAVfOE+7hM3zsOZq\nIgLPde1mMGG/XiJs+ZlrbHrmJUiISAT4PnAGcChwgYgcmpZsLdCrqgcCVwCX58MtW3p6eoJW8IXz\ntEvYPKvKIqzcp5oxhcdycDcRtvzMNTY9S61taWqOAzar6hYAEbkOOAd4MiXNOcCl3vQNwPdERFQ1\n98NiZUGB603gPO0SRs9XLpzPpvZBvvP3F7n6IbuXjpGREcrKCn9An6A9Lz1tfxbWVEybzub3nq8g\nsRh4KWV+G3D8ZGlUdVREYkAD0JWaqKOjg7Vr11JaWkoymWTNmjWsW7eOtrY2qquriUQi9PX10dTU\nRE9PD6pKU1MT7e3tzJ9vKt8GBgZobm6ms7MTEaG+vp7Ozk5qa2tJJpMMDg7S0tJCW1sbZWVlRKNR\nurq6iEajJBIJhoaGJtaXlJQwMDBAd3c3dXV1DA0NEY/HJ9ZXVlZSVVVFb28vDQ0N9Pf3k0gkJtZX\nVVVRXl5OLBajsbGRWCzGyMjIxHpbx5RIJBgeHvZ1TOXl5dTU1ARyTIlEgu7ubuvfk+1jGhsbo7W1\n1fr3lItjam1ttfI9ragaRoCeoVF6hkbtXiEAyE3zWvsE59k3sBvt75729zQ6Oko8Ht/r3Jstko9/\nRCJyPnC6ql7kzV8IHKeqH0lJs8lLs82bf95L0526rfXr1+vKlStz7uyX1tZWli5dGrTGtDhPu4TV\ns3twhFjcfoDYuXMHCxcusr5d2wTtuSRaQXnp9LUE6d/7hg0bHl61atWxs9lnvu4ktgH7pswvAXZM\nkmabiJQCUaDgCwCzjdL5wnnaJayeDdVlNFSXWd0mQJQFNDQU/lgNxeJp83vPV+umB4EVIrJcRMqB\ntwI3paW5CXiXN30ecGeh10c4HA7HXCcvQUJVR4EPA7cCTwHXq+omEfmyiJztJfsZ0CAim4FPAC9r\nJluIDAzkdjAWWzhPuzhPuzhPu9j0zFdxE6p6M3Bz2rIvpEzHgfPz5WOL5ubmoBV84Tzt4jzt4jzt\nYtPTPXGdJZ2dnUEr+MJ52sV52sV52sWmpwsSWSIiQSv4wnnaxXnaxXnaxaanCxJZUl9fH7SCL5yn\nXZynXZynXWx6uiCRJWG8/cwlztMuztMuYfR0QSJLamtrg1bwhfO0i/O0i/O0i01PFySyJJksjq4E\nnKddnKddnKddbHq6IJElg4O5GanLNs7TLs7TLs7TLjY9XZDIkjAOjJ5LnKddnKddwujpgkSWhHFg\n9FziPO3iPO0SRk8XJLLk97//fdAKvnCednGednGedrHp6YJEltx4441BK/jCedrFedrFedrFpqcL\nElkyOpqLwVfs4zzt4jzt4jztYtMzL4MO2eSOO+7oBFqD9hinp6ensb6+vmv6lMHiPO3iPO3iPO2S\nwXPpqlWrmmazraILEg6Hw+HIH664yeFwOByT4oKEw+FwOCbFBQkfiEhERDaKyJ+8+eUicr+IPCci\nv/aGZEVEKrz5zd76ZXl0XCAiN4jI0yLylIicKCL1InK753m7iNR5aUVEvut5PiYiR+fR8+MisklE\nnhCRX4lIZSHkp4hcJSIdIvJEyrIZ55+IvMtL/5yIvCvTvnLg+U3ve39MRH4nIgtS1l3ieT4jIqen\nLF/tLdssItZHgczkmbLu30VERaTRmy+o/PSWf8TLn00i8o2U5QWTnyJypIjcJyKPiMhDInKct9xu\nfqqqe03zwgynei3wJ2/+euCt3vSVwAe96Q8BV3rTbwV+nUfHa4CLvOlyYAHwDeAz3rLPAJd702cC\nfwYEOAG4P0+Oi4GtQFVKPr67EPITeB1wNPBEyrIZ5R9QD2zx3uu86bo8eJ4GlHrTl6d4Hgo8ClQA\ny4HngYj3eh7Y3ztXHgUOzbWnt3xfzDDGrUBjgebn64G/ABXe/D6FmJ/AbcAZKXl4Vy7y091JTIOI\nLAHeBPzUmxfgDcANXpJrgDd70+d483jrV3npc+1YizmJfgagqglV3ZXmk+75czXcBywQkYW59vQo\nBapEpBSYB+ykAPJTVe8BetIWzzT/TgduV9UeVe0FbgdW59pTVW9TM448wH3AkhTP61R1WFW3ApuB\n47zXZlXdoqoJ4DovbU49Pa4APgWktpgpqPwEPgh8XVWHvTQdKZ6FlJ8KjHf3GgV2pHhay08XJKbn\n25iTesybbwB2pfwot2H+IeO9vwTgrY956XPN/kAn8D9iisV+KiLVQLOq7vR8dgL7pHt6pB5DzlDV\n7cD/A17EBIcY8DCFl5/jzDT/AsnXNN6L+RfJFD6BeIrI2cB2VX00bVVBeQIHAa/1ijjvFpFXFajn\nx4BvishLmN/VJbnwdEFiCkTkLKBDVR9OXZwhqfpYl0tKMbeiP1TVo4BBTPHIZATi6ZXpn4O5VV8E\nVANnTOESVH5Ox2RegfqKyGeBUeCX44sm8cm7p4jMAz4LfCHT6kl8gvw91WGKaj4JXO/dwRaa5weB\nj6vqvsDH8UoSpvCZlTlZqVUAAAaQSURBVKcLElPzGuBsEXkBcwv5BsydxQKvuATMrf34bd42TJkr\n3voomW+5bbMN2Kaq93vzN2CCRvt4MZL33pGSft+Uz6ceQy45Fdiqqp2qOgLcCLyawsvPcWaaf0Hl\nK14l5FnA29UrgC4wzwMwfw4e9X5PS4ANItJSYJ54+73RK655AFOK0FiAnu/C/IYAfoMp9mIKn1l5\nuiAxBap6iaouUdVlmIrTO1X17cBfgfO8ZO8C/uBN3+TN462/M+UHm0vPNuAlETnYW7QKeDLNJ93z\nnV4riBOA2HixSo55EThBROZ5/8zGPQsqP1OYaf7dCpwmInXeXdNp3rKcIiKrgU8DZ6vq7jT/t4pp\nJbYcWAE8ADwIrBDTqqwcc27flEtHVX1cVfdR1WXe72kbcLR37hZUfgK/x/whREQOwlRGd1FA+emx\nAzjZm34D8Jw3bTc/bdbAz+UXcAp7Wjftjzk5NmMi+HgriEpvfrO3fv88+h0JPAQ8hjnJ6zDl93d4\nJ88dQL2XVoDvY1pkPA4cm0fPLwFPA08A/4tpKRJ4fgK/wtSTjGAuYGtnk3+YOoHN3us9efLcjClr\nfsR7XZmS/rOe5zN4LWG85WcCz3rrPpsPz7T1L7CndVOh5Wc58AvvHN0AvKEQ8xM4CVOn9yhwP3BM\nLvLTdcvhcDgcjklxxU0Oh8PhmBQXJBwOh8MxKS5IOBwOh2NSXJBwOBwOx6S4IOFwOByOSXFBwuGY\nY3jPoTwtXi+r06QVryuXFflwcxQfLkg4AkdEBlJeYyIylDL/9qD9skFE2kTkpDzvdh1wi6p2eQ7X\nicjnUpyO9Lqd/rCaNvBXAJfm2dFRJLgg4QgcVZ0//sI8lf1PKct+Od3ngyKlK5FC28cHMA8qZtre\nqzDdYH9WVb/nLb4ReJOI5LPzREeR4IKEo+ARM+jT50Vki4h0icgvxRtYR0RWisioiKwVke0i0i0i\n7xUz6NITIrJLRP4rZVsXi8idIvIjEekTkSdF5HUp6+tF5OfeHcBLIvJFESlJ++z3RaQX+Iy3/7tE\npEdEOkXkGhGp8dL/BtNz7G3eXdFHxQxOsznt+CbuNkTk6yJyrZjBlvox3UBMevwZ8uogb58bMqx7\nDaYbho+r6k/Gl6vqAObJ3FNn8/045jYuSDiKgU9i+pk5CdMp2QimiGScCPAKTPce7wH+G/h3TL82\nrwDeIyLHp6R/HaYrgwbg68DvxYzJAaYH1Zi3reMwY0hcmPbZRzAdvn3LW/ZloAU4AjgY03UDqno+\nplPA07y7ou/6PN63YMaviAK/9XH8qRwBPKcv70rhNcCfgItVNdNdxlPAK336OUKECxKOYuADmBHi\ndqhqHNP/0794nQSO82U1g8GMd6z2c1XtVtUXgXuBo1LSvqSqP1DVEVX9OaYvnNNFZCkmCHxCVXer\n6RTtu5gO28bZoqo/UdWkqg6p6tOqeqeagZ7aML0En0x23K2qN6vqmKoO+Tz+cRYA/RmWvwYz5sjt\nk+yz3/usw7EXOS9TdTiywbsQ7gvcLCKp/45L2DMAUVJVu1PWDQHtafPzU+a3pe2mFTO+xVJMp4Kd\nKdffEkxnaOOkDtqCiCwCvoPp8rzGS59tj7oT+/Bx/F1pn+31PNK5AtMJ5K0icqqq9qWtr8mwLYfD\n3Uk4Chuv2GQ7pifOBSmvyvHWO7NgSdr8fphul18CBjDj/o7vp1ZVj05Jm16M803MIE+Hq2otcBF7\nD+6Snn4QM2wrACJShhlzOJWJz8zi+B8DDsxwlzECnA90YwJOddr6QzBFcA7HXrgg4SgGrgS+LiLj\nAxDtIyL/lMX29vUqoUtF5B2YIHGbmnGL7wO+ISI1IlIiIiumacJagwksfSKyH/CJtPXtmPqNcZ4C\n6kVklRcgvsT0v0Pfx6+qm719HpVhXQI4F4gDfxSRKm971Zi6jDum8XCEEBckHMXANzDNNu/0Wvzc\nixl5b7bcg7mI9mAqmc9V1Zi37gJM2fzT3vpfA81TbOsLmArlGPA7TEVzKpcBl3mtrD7s/fv/V0wF\n+TagjemLeWZ6/D9i78r2Cbw6jbMxlf2/E5EKYA1wcxZ3Zo45jBtPwhEqRORi4DxVnbPNPb07hI3A\nSdNd+L1iqYeBt6rqs/nwcxQXruLa4ZhjeC2iVvpMq2R3V+aY47jiJofD4XBMiitucjgcDsekuDsJ\nh8PhcEyKCxIOh8PhmBQXJBwOh8MxKS5IOBwOh2NSXJBwOBwOx6S4IOFwOByOSfn/SaLy2VYm+N4A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs T [X(AL)=0.25]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "plt.gca().set_ylim((-0.1,1.1))\n",
    "# Generate a list of all indices where B2 is stable\n",
    "phase_indices = np.nonzero(eq.Phase.values == 'B2_BCC')\n",
    "# phase_indices[1] refers to all temperature indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq['T'].values, phase_indices[1]), eq['degree_of_ordering'].values[phase_indices])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the heat capacity curve shown below we notice a sharp increase in the heat capacity around 750 K. This is indicative of a magnetic phase transition and, indeed, the temperature at the peak of the curve coincides with 75% of 1043 K, the Curie temperature of pure Fe. (Pure bcc Al is paramagnetic so it has an effective Curie temperature of 0 K.)\n",
    "\n",
    "We also observe a sharp jump in the heat capacity near 1800 K, corresponding to the melting of the bcc phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z4WjveKyEdY0Jnuwa4amuEc5cHt4lGKOaV6y3WWZ6Z3dFDXJq7FwYHacwGyLy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\ngiAoEZVCEARBUOL/MGxs+Yg18LEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Heat capacity vs T [X(AL)=0.25]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Heat Capacity (J/mol-atom-K)')\n",
    "# np.squeeze is used to remove all dimensions of size 1\n",
    "# For a 1-D/\"step\" calculation, this aligns the temperature and heat capacity arrays\n",
    "# In 2-D/\"map\" calculations, we'd have to explicitly select the composition of interest\n",
    "plt.plot(eq['T'].values, np.squeeze(eq['heat_capacity'].values))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand more about what's happening around 700 K, we plot the degree of ordering versus composition. Note that this plot excludes all other phases except `B2_BCC`. We observe the presence of disordered bcc (A2) until around 13% Al or Fe, when the phase begins to order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mh3h1WyuXPrguLVpKBbF2B9c65SmlHsaouI5fdmXc6zXAEU6mIRpNjzupkZDK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\naJWwiIiIVgmLiIho9TdpiYYAMnFAOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs X(AL) [T=700 K]')\n",
    "plt.gca().set_xlabel('X(AL)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "# Generate a list of all indices where B2 is stable\n",
    "phase_indices = np.nonzero(eq2.Phase.values == 'B2_BCC')\n",
    "# phase_indices[2] refers to all composition indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq2['X_AL'].values, phase_indices[2]), eq2['degree_of_ordering'].values[phase_indices])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Al-Ni (Degree of Ordering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 110, X_AL: 1, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 320.0 340.0 360.0 380.0 400.0 ...\n",
      "  * X_AL                (X_AL) float64 0.1\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'NI'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 0.6363 0.3637 0.6457 ...\n",
      "    GM                  (P, T, X_AL) float64 -2.526e+04 -2.585e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -1.719e+05 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 0.01427 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 0.01427 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U7 'FCC_L12' 'FCC_L12' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 1.429e-10 1.0 9.578e-11 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.7+5.g20149e02.dirty\n",
      "    created:  2018-04-18T19:29:04.721929\n"
     ]
    }
   ],
   "source": [
    "db_alni = Database('NI_AL_DUPIN_2001.TDB')\n",
    "phases = ['LIQUID', 'FCC_L12']\n",
    "eq_alni = equilibrium(db_alni, ['AL', 'NI', 'VA'], phases, {v.X('AL'): 0.10, v.T: (300, 2500, 20), v.P: 101325},\n",
    "                      output='degree_of_ordering')\n",
    "print(eq_alni)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "In the plot below we observe two phases designated `FCC_L12`. This is indicative of a miscibility gap. The ordered gamma-prime phase steadily decreases in amount with increasing temperature until it completely disappears around 750 K, leaving only the disordered gamma phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10eaad588>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbwLjcylSCNL1uy7UcxvJWO4X7tzCY9nPshvY9rPsFjVBq6p+BdwjImeJyMmJ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\nJL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycalphad.plot.utils import phase_legend\n",
    "phase_handles, phasemap = phase_legend(phases)\n",
    "\n",
    "plt.gca().set_title('Al-Ni: Phase fractions vs T [X(AL)=0.1]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Phase Fraction')\n",
    "plt.gca().set_ylim((0,1.1))\n",
    "plt.gca().set_xlim((300, 2000))\n",
    "\n",
    "for name in phases:\n",
    "    phase_indices = np.nonzero(eq_alni.Phase.values == name)\n",
    "    plt.scatter(np.take(eq_alni['T'].values, phase_indices[1]), eq_alni.NP.values[phase_indices], color=phasemap[name])\n",
    "plt.gca().legend(phase_handles, phases, loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot below we see that the degree of ordering does not change at all in each phase. There is a very abrupt disappearance of the completely ordered gamma-prime phase, leaving the completely disordered gamma phase. This is a first-order phase transition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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upqWlherqahKJBN3d3X355eXldHZ2Ul5eTk1NDd3d3fT09PTlV1RUUFlZSVtb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\nbvD+y8AtwfuTgLsL7Pkr4MzgfTmwJXA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fbc2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Ni: Degree of fcc ordering vs T [X(AL)=0.1]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "plt.gca().set_ylim((-0.1,1.1))\n",
    "# Generate a list of all indices where FCC_L12 is stable and ordered\n",
    "L12_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
    "                                              (eq_alni.degree_of_ordering.values > 0.01)))\n",
    "# Generate a list of all indices where FCC_L12 is stable and disordered\n",
    "fcc_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
    "                                              (eq_alni.degree_of_ordering.values <= 0.01)))\n",
    "# phase_indices[1] refers to all temperature indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq_alni['T'].values, L12_phase_indices[1]), eq_alni['degree_of_ordering'].values[L12_phase_indices],\n",
    "            label='$\\gamma\\prime$ (ordered fcc)', color='red')\n",
    "plt.plot(np.take(eq_alni['T'].values, fcc_phase_indices[1]), eq_alni['degree_of_ordering'].values[fcc_phase_indices],\n",
    "            label='$\\gamma$ (disordered fcc)', color='blue')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:espei]",
   "language": "python",
   "name": "conda-env-espei-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
